Bottom Line:
A comprehensive feature space was constructed to include network properties within local brain regions, between brain regions, and across the whole network.A leave-one-patient-out cross validation was carried out on 12 patients and a prediction accuracy of 83% was achieved.The importance of selected features was analyzed to demonstrate the contribution of resting-state connectivity attributes at voxel, region, and network levels to TLE lateralization.

Affiliation: School of Information Technology and Electrical Engineering, The University of Queensland , Brisbane, QLD , Australia ; Centre for Advanced Imaging, The University of Queensland , Brisbane, QLD , Australia.

ABSTRACTLateralization of temporal lobe epilepsy (TLE) is critical for successful outcome of surgery to relieve seizures. TLE affects brain regions beyond the temporal lobes and has been associated with aberrant brain networks, based on evidence from functional magnetic resonance imaging. We present here a machine learning-based method for determining the laterality of TLE, using features extracted from resting-state functional connectivity of the brain. A comprehensive feature space was constructed to include network properties within local brain regions, between brain regions, and across the whole network. Feature selection was performed based on random forest and a support vector machine was employed to train a linear model to predict the laterality of TLE on unseen patients. A leave-one-patient-out cross validation was carried out on 12 patients and a prediction accuracy of 83% was achieved. The importance of selected features was analyzed to demonstrate the contribution of resting-state connectivity attributes at voxel, region, and network levels to TLE lateralization.

Figure 3: Inter-regional resting-state functional connectivity. (A) The top 10 FCs with smallest p value in group comparison. (B) The top 10 FCs selected by RF as features. Top: 3D rendering demonstrating the FCs. The nodal size is proportional to the nodal degree. Bottom: the AAL ROI names of the identified regions.

Mentions:
As illustrated in Figure 2, 50 FCs demonstrated significant between-group differences. The top 10 FCs with significant between-group difference and the top 10 selected FCs are shown in Figure 3. It is noted that there was no overlap between the two sets of FCs.

Figure 3: Inter-regional resting-state functional connectivity. (A) The top 10 FCs with smallest p value in group comparison. (B) The top 10 FCs selected by RF as features. Top: 3D rendering demonstrating the FCs. The nodal size is proportional to the nodal degree. Bottom: the AAL ROI names of the identified regions.

Mentions:
As illustrated in Figure 2, 50 FCs demonstrated significant between-group differences. The top 10 FCs with significant between-group difference and the top 10 selected FCs are shown in Figure 3. It is noted that there was no overlap between the two sets of FCs.

Bottom Line:
A comprehensive feature space was constructed to include network properties within local brain regions, between brain regions, and across the whole network.A leave-one-patient-out cross validation was carried out on 12 patients and a prediction accuracy of 83% was achieved.The importance of selected features was analyzed to demonstrate the contribution of resting-state connectivity attributes at voxel, region, and network levels to TLE lateralization.

Affiliation:
School of Information Technology and Electrical Engineering, The University of Queensland , Brisbane, QLD , Australia ; Centre for Advanced Imaging, The University of Queensland , Brisbane, QLD , Australia.

ABSTRACTLateralization of temporal lobe epilepsy (TLE) is critical for successful outcome of surgery to relieve seizures. TLE affects brain regions beyond the temporal lobes and has been associated with aberrant brain networks, based on evidence from functional magnetic resonance imaging. We present here a machine learning-based method for determining the laterality of TLE, using features extracted from resting-state functional connectivity of the brain. A comprehensive feature space was constructed to include network properties within local brain regions, between brain regions, and across the whole network. Feature selection was performed based on random forest and a support vector machine was employed to train a linear model to predict the laterality of TLE on unseen patients. A leave-one-patient-out cross validation was carried out on 12 patients and a prediction accuracy of 83% was achieved. The importance of selected features was analyzed to demonstrate the contribution of resting-state connectivity attributes at voxel, region, and network levels to TLE lateralization.